The causative agent of African sleeping sickness, Trypanosoma brucei, is a unicellular parasite of major biomedical significance and is also an important experimental system for studying small RNAs. Indeed, the discovery of guide RNAs that direct U-insertion/deletion editing of mitochondrial mRNA was the first beacon in this immense area of biology. However, two interconnected areas remain poorly understood: the complexity of the parasite's mitochondrial genome, which is composed of thousands of minicircles and few maxicircles, and the functionality of small RNAs encoded by mitochondrial DNA. This project will generate a complete reference genome from a representative strain, identify and map short RNA transcripts and annotate guide and non- guide RNAs based on in silico predictions and functional assays. We hypothesize that non-guide RNAs produced by antisense transcription play an essential role in the nucleolytic processing of guide RNA precursors and propose to: 1) Build a comprehensive relational database of the mitochondrial genome and transcriptome in T. brucei. We will use the Single Molecule Real Time (SMRT) and paired-end sequencing by Synthesis (SBS) platforms to assemble and annotate a non-redundant set of minicircles and reconstitute the divergent repeat-containing region of the maxi circle. Strand-specific RNA-Seq approaches will be applied to determine a complete repertoire of small mitochondrial RNAs, investigate their 32 end processing and uridylation, and to map these RNAs to genomic locations. Sequencing of polyadenylated mRNAs will be used to assess the fidelity of RNA editing process and to establish the most prominent editing intermediates, misediting and alternative editing events. Computer algorithms will be developed to predict gRNAs based on known and newly-identified editing patterns. 2) Explore relationships between small RNA processing and stabilization, and mRNA editing. We will perturb specific steps in mitochondrial RNA processing and monitor changes in small RNA population to distinguish guide and non-guide RNAs based on their participation in the editing process. These data will be cross-referenced with RNAs bound to editing complexes and computationally-predicted gRNAs.